# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BERT finetuning runner."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import csv
import os
import modeling
import optimization
import tokenization
import tensorflow as tf
import scipy

flags = tf.flags

FLAGS = flags.FLAGS

## Required parameters
flags.DEFINE_string(
    "data_dir", None,
    "The input data dir. Should contain the .tsv files (or other data files) "
    "for the task.")

flags.DEFINE_string(
    "bert_config_file", None,
    "The config json file corresponding to the pre-trained BERT model. "
    "This specifies the model architecture.")

flags.DEFINE_string("task_name", None, "The name of the task to train.")

flags.DEFINE_string("vocab_file", None,
                    "The vocabulary file that the BERT model was trained on.")

flags.DEFINE_string(
    "output_dir", None,
    "The output directory where the model checkpoints will be written.")

## Other parameters

flags.DEFINE_string(
    "init_checkpoint", None,
    "Initial checkpoint (usually from a pre-trained BERT model).")

flags.DEFINE_bool(
    "do_lower_case", True,
    "Whether to lower case the input text. Should be True for uncased "
    "models and False for cased models.")

flags.DEFINE_integer(
    "max_seq_length", 128,
    "The maximum total input sequence length after WordPiece tokenization. "
    "Sequences longer than this will be truncated, and sequences shorter "
    "than this will be padded.")

flags.DEFINE_bool("do_train", False, "Whether to run training.")

flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")

flags.DEFINE_bool("do_test", False, "Whether to run test on the test set.")  # ADDED

flags.DEFINE_bool("benchmark", False, "Whether to run a quick benchmark on prediction or not.")  # ADDED

flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")

flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")

flags.DEFINE_integer("test_batch_size", 8, "Total batch size for eval.")

flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")

flags.DEFINE_float("num_train_epochs", 3.0,
                   "Total number of training epochs to perform.")

flags.DEFINE_float(
    "warmup_proportion", 0.1,
    "Proportion of training to perform linear learning rate warmup for. "
    "E.g., 0.1 = 10% of training.")

flags.DEFINE_integer("save_checkpoints_steps", 1000,
                     "How often to save the model checkpoint.")

flags.DEFINE_integer("iterations_per_loop", 1000,
                     "How many steps to make in each estimator call.")

flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")

tf.flags.DEFINE_string(
    "tpu_name", None,
    "The Cloud TPU to use for training. This should be either the name "
    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
    "url.")

tf.flags.DEFINE_string(
    "tpu_zone", None,
    "[Optional] GCE zone where the Cloud TPU is located in. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string(
    "gcp_project", None,
    "[Optional] Project name for the Cloud TPU-enabled project. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")

flags.DEFINE_integer(
    "num_tpu_cores", 8,
    "Only used if `use_tpu` is True. Total number of TPU cores to use.")

# flags.mark_flag_as_required("data_dir")
# flags.mark_flag_as_required("task_name")
# flags.mark_flag_as_required("vocab_file")
# flags.mark_flag_as_required("bert_config_file")
# flags.mark_flag_as_required("output_dir")
FLAGS.data_dir = r'E:\tmp\0622\sstsb_tf'
FLAGS.task_name = 'sts'
dir_bert = r'E:\deep_learning_model\tf_google_bert模型\chinese_L-12_H-768_A-12\chinese_L-12_H-768_A-12'
FLAGS.vocab_file = dir_bert + '/vocab.txt'
FLAGS.bert_config_file = dir_bert + '/bert_config.json'
FLAGS.init_checkpoint = dir_bert + '/bert_model.ckpt'
FLAGS.output_dir = './out_put'
FLAGS.train_batch_size = 8
FLAGS.max_seq_length = 256
# FLAGS.do_train = True
FLAGS.do_eval = True


class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, label=None):
        """Constructs a InputExample.

        Args:
          guid: Unique id for the example.
          text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
          text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
          label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    # ADDED
    def get_test_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with tf.gfile.Open(input_file, "r") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                lines.append(line)
            return lines


### NOT FROM THE OFFICIAL REPO
class SickProcessor(DataProcessor):
    """Processor for the SICK data set (normalized version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")

    # ADDED
    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0]))
            text_a = tokenization.convert_to_unicode(line[1])
            text_b = tokenization.convert_to_unicode(line[2])
            label = float(line[4])
            examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples

import json
class StsProcessor(DataProcessor):
    """Processor for the STS-B data set."""
    def _read_tsv(self, input_file, quotechar=None):
        with open(input_file, encoding='utf-8') as f:
            for line in f:
                data = json.loads(line)
                yield data
    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")

    # ADDED
    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            qid = line['qid']
            text_a = line['question']
            text_b = line['answer']
            label = line['score']
            label = 5*label
            guid = "%s-%s" % (set_type, qid)

            # guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0]))
            # text_a = tokenization.convert_to_unicode(line[-3])
            # text_b = tokenization.convert_to_unicode(line[-2])
            # label = float(line[-1])
            examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples


###

def convert_examples_to_features(examples, label_list, max_seq_length,
                                 tokenizer, shut_up=False):
    """Loads a data file into a list of `InputBatch`s."""

    #   label_map = {}
    #   for (i, label) in enumerate(label_list):
    #     label_map[label] = i

    features = []
    for (ex_index, example) in enumerate(examples):
        tokens_a = tokenizer.tokenize(example.text_a)

        tokens_b = None
        if example.text_b:
            tokens_b = tokenizer.tokenize(example.text_b)

        if tokens_b:
            # Modifies `tokens_a` and `tokens_b` in place so that the total
            # length is less than the specified length.
            # Account for [CLS], [SEP], [SEP] with "- 3"
            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[0:(max_seq_length - 2)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids: 0   0   0   0  0     0 0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens = []
        segment_ids = []
        tokens.append("[CLS]")
        segment_ids.append(0)
        for token in tokens_a:
            tokens.append(token)
            segment_ids.append(0)
        tokens.append("[SEP]")
        segment_ids.append(0)

        if tokens_b:
            for token in tokens_b:
                tokens.append(token)
                segment_ids.append(1)
            tokens.append("[SEP]")
            segment_ids.append(1)

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1] * len(input_ids)

        # Zero-pad up to the sequence length.
        while len(input_ids) < max_seq_length:
            input_ids.append(0)
            input_mask.append(0)
            segment_ids.append(0)

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        # label_id = label_map[example.label]
        if not shut_up:
            label_id = example.label
            if ex_index < 5:
                tf.logging.info("*** Example ***")
                tf.logging.info("guid: %s" % (example.guid))
                tf.logging.info("tokens: %s" % " ".join(
                    [tokenization.printable_text(x) for x in tokens]))
                tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
                tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
                tf.logging.info(
                    "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
                tf.logging.info("label: {} (id = {})".format(example.label, label_id))

        features.append(
            InputFeatures(
                input_ids=input_ids,
                input_mask=input_mask,
                segment_ids=segment_ids,
                label_id=label_id))
    return features


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()


def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
                 labels, use_one_hot_embeddings):
    """Creates a classification model."""
    model = modeling.BertModel(
        config=bert_config,
        is_training=is_training,
        input_ids=input_ids,
        input_mask=input_mask,
        token_type_ids=segment_ids,
        use_one_hot_embeddings=use_one_hot_embeddings)

    # In the demo, we are doing a simple classification task on the entire
    # segment.
    #
    # If you want to use the token-level output, use model.get_sequence_output()
    # instead.
    output_layer = model.get_pooled_output()

    hidden_size = output_layer.shape[-1].value

    output_weights = tf.get_variable(
        "output_weights", [1, hidden_size],
        initializer=tf.truncated_normal_initializer(stddev=0.02))

    output_bias = tf.get_variable(
        "output_bias", [1], initializer=tf.zeros_initializer())

    with tf.variable_scope("loss"):
        if is_training:
            # I.e., 0.1 dropout
            output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)

        logits = tf.matmul(output_layer, output_weights, transpose_b=True)
        logits = tf.nn.bias_add(logits, output_bias)
        logits = tf.squeeze(logits, [-1])
        # log_probs = tf.nn.log_softmax(logits, axis=-1)

        # one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
        per_example_loss = tf.square(logits - labels)

        # per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
        loss = tf.reduce_mean(per_example_loss)

        return (loss, per_example_loss, logits)


def model_fn_builder(bert_config, init_checkpoint, learning_rate,
                     num_train_steps, num_warmup_steps, use_tpu,
                     use_one_hot_embeddings):
    """Returns `model_fn` closure for TPUEstimator."""

    def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
        """The `model_fn` for TPUEstimator."""

        tf.logging.info("*** Features ***")
        for name in sorted(features.keys()):
            tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))

        input_ids = features["input_ids"]
        input_mask = features["input_mask"]
        segment_ids = features["segment_ids"]
        label_ids = features["label_ids"]

        is_training = (mode == tf.estimator.ModeKeys.TRAIN)

        (total_loss, per_example_loss, logits) = create_model(
            bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
            use_one_hot_embeddings)

        tvars = tf.trainable_variables()

        scaffold_fn = None
        if init_checkpoint:
            (assignment_map,
             initialized_variable_names) = modeling.get_assigment_map_from_checkpoint(
                tvars, init_checkpoint)
            if use_tpu:

                def tpu_scaffold():
                    tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
                    return tf.train.Scaffold()

                scaffold_fn = tpu_scaffold
            else:
                tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

        tf.logging.info("**** Trainable Variables ****")
        for var in tvars:
            init_string = ""
            if var.name in initialized_variable_names:
                init_string = ", *INIT_FROM_CKPT*"
            tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape,
                            init_string)

        output_spec = None
        if mode == tf.estimator.ModeKeys.TRAIN:

            train_op = optimization.create_optimizer(
                total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)

            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=total_loss,
                train_op=train_op,
                scaffold_fn=scaffold_fn)
        elif mode == tf.estimator.ModeKeys.EVAL:

            #       def metric_fn(per_example_loss, label_ids, logits):
            #         predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
            #         accuracy = tf.metrics.accuracy(label_ids, predictions)
            #         loss = tf.metrics.mean(per_example_loss)
            #         return {
            #             "eval_accuracy": accuracy,
            #             "eval_loss": loss,
            #         }
            def metric_fn(per_example_loss, label_ids, logits):
                # Display labels and predictions
                concat1 = tf.contrib.metrics.streaming_concat(logits)
                concat2 = tf.contrib.metrics.streaming_concat(label_ids)

                # Compute Pearson correlation
                pearson = tf.contrib.metrics.streaming_pearson_correlation(logits, label_ids)

                # Compute MSE
                # mse = tf.metrics.mean(per_example_loss)
                mse = tf.metrics.mean_squared_error(label_ids, logits)

                # Compute Spearman correlation
                size = tf.size(logits)
                indice_of_ranks_pred = tf.nn.top_k(logits, k=size)[1]
                indice_of_ranks_label = tf.nn.top_k(label_ids, k=size)[1]
                rank_pred = tf.nn.top_k(-indice_of_ranks_pred, k=size)[1]
                rank_label = tf.nn.top_k(-indice_of_ranks_label, k=size)[1]
                rank_pred = tf.to_float(rank_pred)
                rank_label = tf.to_float(rank_label)
                spearman = tf.contrib.metrics.streaming_pearson_correlation(rank_pred, rank_label)

                return {'pred': concat1, 'label_ids': concat2, 'pearson': pearson, 'spearman': spearman, 'MSE': mse}

            eval_metrics = (metric_fn, [per_example_loss, label_ids, logits])
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=total_loss,
                eval_metrics=eval_metrics,
                scaffold_fn=scaffold_fn)
        else:
            raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))

        return output_spec

    return model_fn


def input_fn_builder(features, seq_length, is_training, drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""

    all_input_ids = []
    all_input_mask = []
    all_segment_ids = []
    all_label_ids = []

    for feature in features:
        all_input_ids.append(feature.input_ids)
        all_input_mask.append(feature.input_mask)
        all_segment_ids.append(feature.segment_ids)
        all_label_ids.append(feature.label_id)

    def input_fn(params):
        """The actual input function."""
        batch_size = params["batch_size"]

        num_examples = len(features)

        # This is for demo purposes and does NOT scale to large data sets. We do
        # not use Dataset.from_generator() because that uses tf.py_func which is
        # not TPU compatible. The right way to load data is with TFRecordReader.
        d = tf.data.Dataset.from_tensor_slices({
            "input_ids":
                tf.constant(
                    all_input_ids, shape=[num_examples, seq_length],
                    dtype=tf.int32),
            "input_mask":
                tf.constant(
                    all_input_mask,
                    shape=[num_examples, seq_length],
                    dtype=tf.int32),
            "segment_ids":
                tf.constant(
                    all_segment_ids,
                    shape=[num_examples, seq_length],
                    dtype=tf.int32),
            "label_ids":
            # tf.constant(all_label_ids, shape=[num_examples, 0], dtype=tf.float32),
                tf.constant(all_label_ids, shape=[num_examples], dtype=tf.float32),
        })

        if is_training:
            d = d.repeat()
            d = d.shuffle(buffer_size=100)

        d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
        return d

    return input_fn


def main(_):
    tf.logging.set_verbosity(tf.logging.INFO)

    processors = {
        "sick": SickProcessor,
        "sts": StsProcessor
    }

    if not FLAGS.do_train and not FLAGS.do_eval:
        raise ValueError("At least one of `do_train` or `do_eval` must be True.")

    bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)

    if FLAGS.max_seq_length > bert_config.max_position_embeddings:
        raise ValueError(
            "Cannot use sequence length %d because the BERT model "
            "was only trained up to sequence length %d" %
            (FLAGS.max_seq_length, bert_config.max_position_embeddings))

    tf.gfile.MakeDirs(FLAGS.output_dir)

    task_name = FLAGS.task_name.lower()

    if task_name not in processors:
        raise ValueError("Task not found: %s" % (task_name))

    processor = processors[task_name]()

    # label_list = processor.get_labels()
    label_list = None

    tokenizer = tokenization.FullTokenizer(
        vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)

    tpu_cluster_resolver = None
    if FLAGS.use_tpu and FLAGS.tpu_name:
        tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
            FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)

    is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
    run_config = tf.contrib.tpu.RunConfig(
        cluster=tpu_cluster_resolver,
        master=FLAGS.master,
        model_dir=FLAGS.output_dir,
        save_checkpoints_steps=FLAGS.save_checkpoints_steps,
        tpu_config=tf.contrib.tpu.TPUConfig(
            iterations_per_loop=FLAGS.iterations_per_loop,
            num_shards=FLAGS.num_tpu_cores,
            per_host_input_for_training=is_per_host))

    train_examples = None
    num_train_steps = None
    num_warmup_steps = None
    if FLAGS.do_train:
        train_examples = processor.get_train_examples(FLAGS.data_dir)
        num_train_steps = int(
            len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
        num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)

    model_fn = model_fn_builder(
        bert_config=bert_config,
        init_checkpoint=FLAGS.init_checkpoint,
        learning_rate=FLAGS.learning_rate,
        num_train_steps=num_train_steps,
        num_warmup_steps=num_warmup_steps,
        use_tpu=FLAGS.use_tpu,
        use_one_hot_embeddings=FLAGS.use_tpu)

    # If TPU is not available, this will fall back to normal Estimator on CPU
    # or GPU.
    estimator = tf.contrib.tpu.TPUEstimator(
        use_tpu=FLAGS.use_tpu,
        model_fn=model_fn,
        config=run_config,
        train_batch_size=FLAGS.train_batch_size,
        eval_batch_size=FLAGS.eval_batch_size)

    if FLAGS.do_train:
        import time
        train_t0 = time.time()
        train_features = convert_examples_to_features(
            train_examples, label_list, FLAGS.max_seq_length, tokenizer)
        tf.logging.info("***** Running training *****")
        tf.logging.info("  Num examples = %d", len(train_examples))
        tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
        tf.logging.info("  Num steps = %d", num_train_steps)
        train_input_fn = input_fn_builder(
            features=train_features,
            seq_length=FLAGS.max_seq_length,
            is_training=True,
            drop_remainder=True)
        estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
        train_t1 = time.time()

    if FLAGS.do_eval:
        eval_examples = processor.get_dev_examples(FLAGS.data_dir)
        eval_features = convert_examples_to_features(
            eval_examples, label_list, FLAGS.max_seq_length, tokenizer)

        tf.logging.info("***** Running evaluation *****")
        tf.logging.info("  Num examples = %d", len(eval_examples))
        tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)

        # This tells the estimator to run through the entire set.
        eval_steps = None
        # However, if running eval on the TPU, you will need to specify the
        # number of steps.
        if FLAGS.use_tpu:
            # Eval will be slightly WRONG on the TPU because it will truncate
            # the last batch.
            eval_steps = int(len(eval_examples) / FLAGS.eval_batch_size)

        eval_drop_remainder = True if FLAGS.use_tpu else False
        eval_input_fn = input_fn_builder(
            features=eval_features,
            seq_length=FLAGS.max_seq_length,
            is_training=False,
            drop_remainder=eval_drop_remainder)

        result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)

        output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
        with tf.gfile.GFile(output_eval_file, "w") as writer:
            tf.logging.info("***** Eval results *****")
            for key in sorted(result.keys()):
                tf.logging.info("  %s = %s", key, str(result[key]))
                writer.write("%s = %s\n" % (key, str(result[key])))

    if FLAGS.do_test:
        test_examples = processor.get_test_examples(FLAGS.data_dir)
        test_features = convert_examples_to_features(
            test_examples, label_list, FLAGS.max_seq_length, tokenizer)

        tf.logging.info("***** Running test *****")
        tf.logging.info("  Num examples = %d", len(test_examples))
        tf.logging.info("  Batch size = %d", FLAGS.test_batch_size)

        # This tells the estimator to run through the entire set.
        test_steps = None
        # However, if running eval on the TPU, you will need to specify the
        # number of steps.
        if FLAGS.use_tpu:
            # Eval will be slightly WRONG on the TPU because it will truncate
            # the last batch.
            test_steps = int(len(test_examples) / FLAGS.test_batch_size)

        test_drop_remainder = True if FLAGS.use_tpu else False
        test_input_fn = input_fn_builder(
            features=test_features,
            seq_length=FLAGS.max_seq_length,
            is_training=False,
            drop_remainder=test_drop_remainder)

        result = estimator.evaluate(input_fn=test_input_fn, steps=test_steps)

        output_test_file = os.path.join(FLAGS.output_dir, "test_results.txt")
        with tf.gfile.GFile(output_test_file, "w") as writer:
            tf.logging.info("***** Test results *****")
            for key in sorted(result.keys()):
                tf.logging.info("  %s = %s", key, str(result[key]))
                writer.write("%s = %s\n" % (key, str(result[key])))

    # Benchmark how long it takes for prediction
    if FLAGS.benchmark:
        import time
        t0 = time.time()
        for i in range(100):
            predict_example = [
                InputExample(guid=2, text_a='He is a smart and experienced person', text_b='He\'s a truly wise man')]
            predict_features = convert_examples_to_features(predict_example, label_list, FLAGS.max_seq_length,
                                                            tokenizer, shut_up=True)
            input_fn = input_fn_builder(features=predict_features, seq_length=FLAGS.max_seq_length, is_training=False,
                                        drop_remainder=False)
            result = estimator.predict(input_fn=input_fn)
        t1 = time.time()
        print("Time necessary for 1 prediction : {}".format((t1 - t0) / 100))
        print("Time necessary for training : {}".format(train_t1 - train_t0))


if __name__ == "__main__":
    tf.app.run()


# INFO:tensorflow:***** Eval results *****
# INFO:tensorflow:  MSE = 2.1216094
# INFO:tensorflow:  global_step = 9000
# INFO:tensorflow:  label_ids = [3.4355445  0.88500303 3.2898877  ... 0.6779364  3.692494   0.54901   ]
# INFO:tensorflow:  loss = 2.1216435
# INFO:tensorflow:  pearson = 0.111888304
# INFO:tensorflow:  pred = [1.7956709 1.7956742 1.795673  ... 1.795669  1.7956736 1.7956738]
# INFO:tensorflow:  spearman = 0.07484919

# to add  边训练 边预测